The authors analyze events that have occurred in the municipality of Konjic throughout the March and April of 1993. Special emphasis was placed on crimes committed against the Croatian population of ...that municipality. In the early morning of April 16, 1993, Army of the Republic of Bosnia and Herzegovina (BiH) committed a war crime in the northern Herzegovinian village of Trusina, where 22 Croatian civilians and captured Croatian Defence Council (HVO) soldiers were killed. This crime was the result of a previously planned attack by the Army of BiH on the Croatian population and the HVO in the Konjic municipality, which began on April 14, 1993. The main attacking forces of the Army of BiH on the village Trusina on April 16, 1993, were members of the Zulfikar Special Purposes Detachment . They were under the direct command of the Supreme Command Staff (SVC) (i.e., General Staff of the Army of BiH) from their formation to just a few days before the crime in Trusina was committed. They have then become an integral part of the 1st Corps of the Army of BiH based in Sarajevo. As an integral part of the 1st Corps, members of the Zulfikar Special Purposes Detachment became the main perpetrators of a previously planned attack and war crime against the Croatian population of Trusina.
On 20 March 2023 the Council of the European Union gave Bosnia and Hercegovina green light to start accession negotiations. However, despite this political endorsement, BiH must fulfill the ...conditionality criteria, including a series of six judgments by the ECtHR relating to the predetermined ethnic keys. The last case, Kovačević v. BiH, was referred to the Grand Chamber in December 2023. If the Court follows its previous case law, this should force the mono-ethnic political parties and their leaders as well as the EU institutions to insist on de-blocking the constitutional impasse for any realistic steps towards European integration.
Electrocardiogram (ECG) can reflect the state of human heart and is widely used in clinical cardiac examination. However, the electrocardiogram signal is very weak, the anti-interference ability is ...poor, easy to be affected by the noise. Doctors face difficulties in diagnosing arrhythmias. Therefore, automatic recognition and classification of ECG signals is an important and indispensable task. Since the beginning of the 21 st century, deep learning has developed rapidly and has shown the most advanced performance in various fields. This paper presents a method of combining (Convolutional neural network) CNN and ELM (extreme learning machine). The accuracy rate is 97.50%. Compared with the state-of-the-art methods, this method improves the accuracy of ECG automatic classification and has good generalization ability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•This work describes a new approach of classifying ECG signals for abnormalities detection.•The DWT and morphology based feature has been extracted for experimental work.•ECG signal analysis includes ...de-noising, feature extraction and classification of signal data.•System can truly classify ECG signal into abnormal and normal class utilizing neural classifier.•The result for training and testing the data sample is greater than 95% using any of the classifier.
This paper deals with ECG signal analysis based on Artificial Neural Network and combined based (discrete wavelet transform and morphology) features. We proposed a technique to truthfully classify ECG signal data into two classes (abnormal and normal class) using various neural classifier. MIT–BIH arrhythmia database utilized and selected 45 files of one minute recording (25 files of normal class and 20 files of abnormal class) out of 48 files based on types of beat present in it. The total 64 features are separated in to two classes that is DWT (48) based features and morphological (16) feature of ECG signal which is set as an input to the classifier. Three neural network classifiers: Back Propagation Network (BPN), Feed Forward Network (FFN) and Multilayered Perceptron (MLP) are employed to classify the ECG signal. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP). The system performance is achieved with 100% accuracy using MLP.
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Manual identification of ECG heart-beat classes by cardiologists is time consuming and cumbersome. These professionals rely on computer based methods for determination of these heart-disease types. ...In this work, existing literature is organized into a proposed taxonomy based on dichotomies involving full time series-based versus feature-based, AAMI versus Non-AAMI, and inter-patient versus intra-patient based distinctions. The basic contributions of this work are systematic review of literature on heart-beat abnormality detection, identifying research gaps and the research issues unmet sofar in the literature to propose novel approaches for addressing these gaps.
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Electrocardiogram is the P-QRS-T wave representing the cardiac depolarization and re-polarization, recorded at the body surface. The subtle changes in amplitude and duration of these waves indicate ...various pathological conditions. It is very difficult to decipher minute changes in the ECG wave by naked eye. Hence a computer aided diagnosis tool to classify various cardiac diseases will assist the doctors in their ECG reading. In this paper, five types of ECG beats (ANSI/AAMI EC57:1998 standard) of MIT–BIH arrhythmia database were automatically classified. Our proposed methodology involves computation of Discrete Cosine Transform (DCT) coefficients from the segmented beats of ECG, which were then subjected for principal component analysis for dimensionality reduction. Then the clinically significant principal components were fed to (i) feed forward neural network, (ii) least square support vector machine with different kernel functions, and (iii) Probabilistic Neural Network (PNN) for automatic classification. We have obtained the highest average sensitivity of 98.69%, specificity of 99.91%, and classification accuracy of 99.52% with the developed knowledge based system. The developed system is clinically ready to deploy for mass screening programs.
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•In this manuscript, MLGK-TDCNN is proposed for the detection of ECG arrhythmia.•Here, the input ECG signals are taken from two datasets: (i) AFDB (ii) MBDB.•These datasets are balanced using ...Improved fuzzy c-means method.•Moreover, the de-noised ECG signals are given to the MLGK.•Then, the MLGK features given to the TDCNN classifier for detecting AF and NSR.
In this manuscript, Multiscale Laplacian graph kernel features combined with Tree Deep Convolutional Neural Network (MLGK-TDCNN) is proposed for the detection of Electrocardiogram (ECG) arrhythmia. Here, the input ECG signals are taken from two datasets: (i) MIT-BIH AF database (AFDB) (ii) MIT-BIH arrhythmia database (MBDB). These datasets are fully unbalanced dataset, and these datasets are balanced using Improved fuzzy c-means method for unbalanced dataset. Moreover, the de-noised ECG signals are given to the MLGK. The proposed MLGK is to combine the Multiscale kernel features from the Preprocessed ECG signals. The combined Multiscale kernel features given to the TDCNN classifier for the detection of AF with raw normal sinus rhythm (NSR). The proposed approach is activated in MATLAB platform, then the efficiency is analyzed with existing approaches. The experimental outcomes demonstrate that the proposed FFREWT-MLGK-TDCNN approach is compared with two databases. From the analysis, the accuracy of AFDB shows 9.40%, 16.44% and 23.20% better than the existing approaches, the accuracy of MBDB shows 14.67%, 21.42% and 7.54% better than the existing approaches, like novel intelligent approach depending on multi-scale convolution kernel (MCK) and Squeeze-and-Excitation network (SENet) for AF detection, automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (CNN-BaROA), deep learning method for classifying arrhythmia by using 2-second segments of 2D recurrence plot images of ECG signals (2D-CNN) respectively.
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The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that ...refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.
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•Convolutional neural network (CNN) is used to classify 5 ECG classes.•9-layer deep CNN is implemented.•Generated synthetic data to overcome imbalance problem.•Accuracy of 94.03% and 93.47% with and without noise removal respectively.
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Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for ...the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies.
•Deep learning framework using Restricted Boltzmann Machine & Deep Belief Networks is proposed for ECG arrhythmia classification.•The proposed methodology performs robust features extraction for ECG signals at a very low sampling rate of 114 Hz.•Results demonstrate state-of-the-art accuracies at lower sampling rates using proposed framework & simple features.
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